Stereotactic body radiotherapy (SBRT) has shown promising results for patients with recurrent ventricular tachycardia that failed catheter ablation. The simplest method of target delineation based on manual transfer of CTV from several 3D visualizations (electroanatomic mapping system) to planning CT slices (2D) was subjective and very time consuming. Advanced methods are based on merging of electroanatomic maps (EAM) with CT. To date, 2 types of EAM-CT merge were proposed: manual alignment of EAM structures with segmented CT structures [1,2] and use of semi-automatic algorithm [3]. The goal of this work was to modify current method [2] with the use of robust automatic algorithms to assure reasonably short learning curve. This work is based on data of 10 patients who had previously undergone SBRT treatment for ventricular tachycardia (VT). Two observers participated in this study: (1) an electrophysiologist technician (cardiology) with substantial experience in EAM-CT merge, and (2) a clinical engineer (radiotherapy) with minimum experience with EAM-CT merge. Observer (1) performed EAM-CT merge three times for each case with an interval of at least 8h. Observer (2) did EAM-CT merge once for each patient following instructions written by observer 1. EAM-CT merge consist of 3 main steps: segmentation of left ventricle from CT (CT LV), registration of the CT LV and EAM, CTV delineation from EAM specific points. Mean Hausdorff distance (MHD), Dice Similarity Coefficient (DSC) and absolute difference in Center of Gravity (CoG) were used to assess intra/interobserver variability. All quantitative data was expressed as the mean and SD. Intraobserver variability Three segmented CT LVs were compared with each other. The mean DSC was 0,92±0,01 for all cases. The MHD was 1,49 ± 0,23 mm and the mean absolute difference in CoG coordinates was <1.5mm for all cases. The mean DSC and MHD for 3 CTVs altogether was 0,82±0,06 and 0,71 ±0,22 mm. The mean absolute difference in CoG was 0,64mm, 0,95mm and 0,78mm for X, Y, and Z coordinates, respectively. Interobserver variability Segmented CT LVs showed great similarity (mean DSC of 0,91±0,01, MHD of 1,86±0,47 mm) for all cases. The mean DSC comparing CTVs from both observers was 0,81±0,11 and MHD was 0,87±0,45 mm. The difference in CT LV volumes correlates with DSC of compared CTVs (Pearson´s correlation coefficient R = 0,8, p = 0,005) indicating that accurate segmentation of CT LV is crucial for successful precise CTV delineation. Modification of the current method based on incorporating automatic algorithms created a robust and fast method for CTV delineation. High similarity of both, the segmented CT LVs and delineated CTVs between observers confirmed robustness of the proposed method and steep learning curve.